Computers have revolutionized the ability to collect, sort, manipulate, and store data. The data processing capacities of computers have transformed industries from banking to transportation. The data processing abilities of computers have also created a universe of other industries from merchandising to communications that otherwise never would have been possible.
The evolution of display and graphics technologies emerging over the last few decades has further extended the usefulness of computers. It is well documented how much better people can assimilate data presented in the form of graphs or other visual representations as compared to how well they can assimilate the same information presented in the form of text and tables. Because even a commonplace personal computer can transform columns of numbers and text into a colorful, multidimensional graph or chart, computers not only collect, sort, manipulate, and store data, but can also help distill the information into a human-useable form.
FIG. 1 shows a conventional data-processing system 100. The system 100 typically has three principal layers: a data source layer 110, a processing layer 130, and a visualization layer 150. The data source layer 110 generally incorporates a number of data storage devices 120. The data storage devices 120 typically include one or more of direct-access storage devices (DASDs) such as hard disks, diskettes, or CD-ROMs. The processing layer 130 typically incorporates data-processing subsystems of the system 100 such as microprocessors and random access memory devices (RAM) in which operations are performed on data stored in the data source layer 110. The visualization layer 150 incorporates at least one of a display 160 or another device, such as a printer, configured to generate printed output 170. The visualization layer 150 allows raw data stored in the data source layer 110 and/or processed by the processing layer 130 to be presented to the user for review. The information displayed may include charts or graphs selected by the user to try to evaluate the content and/or meaning of the data.
FIG. 2 shows one form of data that it may be desirable to present using a data processing system such as the system 100 (FIG. 1). FIG. 2 shows a calendar month 200 which includes a number of days. For each day of the month, for example a day 210 such Jan. 28, 2002, various event data 220 may be logged in an event log, a portion 230 of which is shown in FIG. 2. Data 220 logged for the day 210 may include one or more events 240 and 250 that occurred on the day 210. In FIG. 2, the data 220 logged in the portion of the event log 230 includes a series of aircraft maintenance events 240 and 250. Each of the events 240 and 250 may include a number of fields such as a date 260, an event type 270, a code 280 indicating the type of event, a location of the event 290, and/or other data (not shown). In the data 220 shown in the portion of the event log 230, for example, for the date 260 of Jan. 28, 2002, the event type 270 may include a broken door, a tail light failure, or another event. The code 280, which might include an Air Transport Association (ATA) code or some other alphabetic, numeric, or alphanumeric coding scheme, includes one code to represent the broken door and another to indicate the tail light failure. The codes 280 listed here are “X” and “Y” but could include any suitable single-digit or multiple-digit coding scheme. The location 290 includes Seattle, Chicago, or another location.
Using the processing layer 130 (FIG. 1), the data 220 stored in the portion of the event log 230 may be correlated by data 260, event type 270, code 280, and/or location 290 to generate reports. Reports might be created to tally how many events of each type transpired to determine if original parts may be failing too frequently. Alternatively, the reports might be developed to help human analysts interpret what type of parts inventory and personnel and/or skills are needed, where the parts are needed, and when.
To better distill frequency of event types, trends, or other information from the data 220 stored in the portion of the event log 230, it may be desirable to generate a chart or a graph. FIG. 3, for example, shows a bar graph 300 that may be generated from the event data 220. The bar graph 300 may collect a number of events 240 and 250 (FIG. 2) that have taken place according to a number of event types 270 or codes 280 or for a day 210, a month 200, or another period of time.
The graph 300 shows a number of events 310 listed according to event type, including events collected for categories such as doors 320, engines 330, electronics 340, and lights 350. The graph 300 may show a number of events for the different categories 320, 330, 340, and 350 for an hour, a day, a week, a month, a year, or another unit of time. Thus, the graph 300 pictorially or graphically represents series of events that have taken place.
Whether the information is useful to a human analyst may depend on what the human analyst seeks to discern from the data represented. For example, if the human analyst is seeking to identify trends, such as times or dates when these events tend to peak, the graph 300 may not be particularly useful. Hypothetically, if graphs 300 were generated for the different categories 320, 330, 340 and 350 for every day of one or more years, the human analyst would have to compare hundreds upon hundreds of graphs looking for trends. Considered in this context, the graphs that might have been relatively useful to compare event totals when looking at one graph or a few graphs at a time now are no longer nearly as helpful.
FIG. 3B illustrates another conventional way of visualizing data, such as data which may be distilled from a portion of an event log 230. More particularly, FIG. 3B shows a line graph 355 that might be used for viewing numbers of occurrences or other measurements occurring over time. The line graph 355 suitably includes one or more lines 360, 370, 380, and 390, each of which recounts a status of a different measurement over time. Although a legend 395 might be included to clarify which of the lines 360, 370, 380, and 390 depicts which measurement, from FIG. 3B one can appreciate that, especially as more and more measurements are added, or more and more graphs 355 are presented the data represented by such a graph 355 may be difficult to assimilate.
Manual evaluation of such data can be time-consuming. Even when appropriate human resources are available to analyze such information, presented with large quantities of data, significant variations in data may be lost; certainly subtle but important variations similarly may be lost.
Thus, there is an unmet in the art for mining time-related data to identify variations of potential interest, and for graphically presenting time-related data spanning long periods of time to facilitate enhanced analysis of the data.